The ability to automatically classify objects into categories of interest has applications across a wide range of industries and scientific fields, including biology, social sciences, and finance. One particular application of interest is the classification of biological cells according to cell phenotype.
An accurate and efficient automated cell phenotype classification method requires identifying morphological characteristics of individual cells and/or sub-cellular objects, as pictured in a digital image, which are useful for distinguishing different cell phenotypes. Thus, when using image processing techniques to perform cell phenotype classification, it is desired to identify morphological features that vary according to the different cell types in an image and are characteristic of those cell types. A cell type having a unique size, for example, may be identified by evaluating the sizes of the cells in the image. Likewise, a cell type having a particular characteristic shape or color may be identified by evaluating the shapes and colors of the cells in the image. The more a morphological feature (e.g., size, shape, or color) varies from one cell type to the next, the more useful that feature is for distinguishing different types of cells during cell phenotype classification.
Identification of sub-cellular objects in images, such as nuclei, micronuclei, cytoplasm, and organelles, may be particularly useful for classifying cell phenotype. However, such objects must be distinguishable from image artifacts, and overlap of multiple sub-cellular objects should be identified as such, rather than erroneously identified as a single object. The most widely used cell detection methods detect nuclei in a first step and whole cells around the nuclei in one or more subsequent steps. It may be relatively easy to detect nuclei since DNA stains are widely known and available.
Automated image processing techniques are useful to standardize the classification process for improved accuracy and speed of cell classification. However, existing automated image processing techniques are often incapable of distinguishing among the different cell phenotypes in an image. Existing image processing techniques can also be overly complicated, difficult to describe and implement, and computationally expensive.
There is a need for more accurate and efficient image processing techniques for identifying different types of objects, such as sub-cellular objects, in an image. In particular, there is a need for new features that may be used to identify and characterize sub-cellular objects in an image, for the purpose of automated cell phenotype classification.